Saikawa_ENVS399R

I. Introduction

With urbanization often comes both prosperity and environmental challenges. Atlanta, a bustling metropolis, embodies this intricate combination between economic growth and ecological hurdles in many realms. This paper sets out to illuminate particularly the challenge of soil contamination and employs spatial analysis techniques to dissect soil contamination data across the greater Atlanta area. Drawing on existing soilSHOP data collected and tested by members of the Saikawa Lab, this project’s aim is to construct a digestible and interactive visualization of soil contamination across Atlanta on a larger scale. Delving beyond raw data points, this paper aims to depict the spatial distribution and severity of soil pollutants while also uncovering the intricate relationship between these environmental hazards and urban economic factors. To enrich the analysis, the focus shifts to overlaying incentive zone data, dissecting the intersection (or lack thereof) between economic opportunity and environmental degradation.

This mission, like every other initiative undertaken by members of the Saikawa Lab at Emory, extends beyond academic curiosity. It is rooted in a greater and more long-term commitment to community impact and sustainable urban development. The purpose of shedding light on the extent of soil contamination in Atlanta is to foster and cultivate community awareness and empower advocacy for more remediation actions. In addition, scrutinizing the incentive zone data in conjunction with lead contamination provides insight into the economic implications of policy initiatives, not only to inspire future research endeavors but also to pave the way for informed decision-making towards a cleaner, healthier Atlanta.

Therefore, this research combines scientific inquiry, social responsibility, and economic foresight. This spatial analysis endeavors to aggregate all the dimensions of soil lead contamination from the Saikawa lab’s soilSHOP initiative with the greater goal of being a long-term project for community awareness, advocacy, and environmental justice.

II. Literature Review

Soil contamination poses significant risks to the environment and public health. Studies, such as that by da Silva et al. (2017), have quantified the levels of various toxic metals in soil samples, revealing widespread contamination in urban areas. Industrial waste, metallurgy, cement production, vehicle exhaust, and vehicular traffic have been identified as the main sources of contamination. This underscores the urgent need for effective measures to mitigate soil contamination and its associated ecological risks. In Atlanta, soil contamination gained considerable attention in 2018 when Emory University-based research identified overwhelming lead contamination in West Atlanta. This discovery led to the establishment of the Westside Lead Superfund Site by the EPA, highlighting the pressing need for remediation efforts in the area. Regarding urban agriculture in Atlanta, Balotin et al. (2020) stress the importance of awareness of heavy metal and metalloid (HMM) contamination in soil due to its potential adverse health effects. Their study revealed disparities in knowledge levels among different groups of gardeners, emphasizing the importance of comprehensive data dissemination and community engagement to improve public awareness and understanding of soil contamination issues. Economic incentives play a crucial role in reducing soil contamination and promoting remediation efforts, as discussed by Bonnieux et al. (1998). Their research focuses on the microeconomics of pollution control and the challenges associated with estimating the costs and benefits of restoration efforts. Advancements in remote sensing and spatial techniques, as highlighted by Hussain et al. (2023), offer valuable tools for assessing and monitoring soil contamination over large areas. Despite challenges such as spectral unmixing and data resolution, these techniques provide rapid and accurate information about the extent and distribution of contaminants in soil. In summary, these articles provide valuable insights into the extent and sources of soil contamination in urban areas, their economic implications, assessment methodologies, and the understanding of soil contamination by Atlanta's population. They underscore the importance of interdisciplinary approaches and effective policy interventions to address soil contamination and safeguard environmental and human health. This paper emphasizes the importance of fostering community awareness through digestible data dissemination and understanding the role of economic factors in potential remediation efforts.

III. Data & Methodology

To address the need for a centralized visualization platform for soil lead contamination data across Atlanta, first an analysis was conducted of past formats and techniques used by the Saikawa Lab. Previous visualizations included Canva birds-eye views, reports to community members, and a priority screening index map on the lab's website. Additionally, doctoral and postdoctoral students have conducted various studies at the zip code and census tract levels. However, there was a lack of a central base visualization that could be updated periodically as new data is collected and interactive for enhanced user engagement.

In order to create a more specific consolidation of soilSHOP data, the focus shifted to enhancing the dataset by adding both census tract numbers and latitude and longitude coordinates to each observation. This involved extracting information from the respective street and nearest intersection entered by community participants when submitting their soil samples. It's important to note that privacy is prioritized and exact addresses from participants are not collected. Instead, reliance is placed on estimated latitude and longitude coordinates, which provide solid approximations given that lead contamination is likely to be relatively consistent within a given area.

Using the newly centralized dataset with inputted locations (n=106), the project moved forward with the development of interactive maps using Python. Geopandas was utilized for static visualizations, while Folium was employed for interactive mapping. Additionally, shapefile sources from the Department of City Planning for zoning overlays were integrated to visualize in conjunction with economic variables. This comprehensive approach allows for a more nuanced understanding of soil contamination patterns in Atlanta, enabling exploration of the relationships between contamination levels, socioeconomic factors, and zoning regulations. By leveraging these tools and datasets, a robust and accessible platform for stakeholders to analyze and address soil contamination issues in the urban environment was aimed for.

Variable (n=106)	Description
lat	Latitude found on Google Maps from intersection provided by participant
long	Longitude found on Google Maps from intersection provided by participant
pbresults_avg	Mean of 2 readings of soil lead level using XRF machine to two decimal places

In [22]:

import folium
from folium.plugins import HeatMap
import geopandas as gpd
import pandas as pd

# Read the CSV file containing latitude, longitude, and lead results
latlong = pd.read_csv("/Users/claudiasmith/Dropbox/My Mac (Claudia’s MacBook Pro)/Downloads/Saikawa Lab DATA/SoilSHOP 2021_updated - LatLong_Combined.csv")

# Drop rows with NaN values
latlong.dropna(subset=['lat', 'long', 'pbresults_avg'], inplace=True)

# Create a map centered around Atlanta
m = folium.Map(location=[33.7490, -84.3880], zoom_start=10)

# Create HeatMap layer
heat_map = HeatMap(latlong[['lat', 'long', 'pbresults_avg']], 
                    radius=10, 
                    min_opacity=0.5, 
                    max_opacity=0.8, 
                    gradient={0.1: 'blue', 0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 0.8: 'red', 0.99: 'purple'})

# Add HeatMap layer to the map
heat_map.add_to(m)

# Add legend for soil contamination spectrum
legend_html = """
<div style="position: fixed; bottom: 50px; left: 50px; z-index:1000; font-size: 14px; background-color: white; padding: 10px; border: 1px solid grey; border-radius: 5px;">
    <p><strong>Soil Contamination Spectrum</strong></p>
    <p><span style="color: blue;">0-50 ppm</span></p>
    <p><span style="color: lime;">50-200 ppm</span></p>
    <p><span style="color: orange;">200-400 ppm</span></p>
    <p><span style="color: red;">400+ ppm</span></p>
</div>
"""
m.get_root().html.add_child(folium.Element(legend_html))

# Read the CSV file containing latitude, longitude, and lead results
latlong = pd.read_csv("/Users/claudiasmith/Dropbox/My Mac (Claudia’s MacBook Pro)/Downloads/Saikawa Lab DATA/SoilSHOP 2021_updated - LatLong2021.csv")

# Drop rows with NaN values
latlong.dropna(subset=['lat', 'long', 'pbresults_avg'], inplace=True)

# Create a map centered around Atlanta
m2 = folium.Map(location=[33.7490, -84.3880], zoom_start=10)

# Create HeatMap layer
heat_map = HeatMap(latlong[['lat', 'long', 'pbresults_avg']], 
                    radius=10, 
                    min_opacity=0.5, 
                    max_opacity=0.8, 
                    gradient={0.1: 'blue', 0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 0.8: 'red', 0.99: 'purple'})

# Add HeatMap layer to the map
heat_map.add_to(m2)

# Read the shapefiles for tax allocation district and federal opportunity zones
federal_opportunity_zones = gpd.read_file("/Users/claudiasmith/Dropbox/My Mac (Claudia’s MacBook Pro)/Downloads/Saikawa Lab DATA/Federal_Opportunity_Zones/Federal_Opportunity_Zones.shp")

# Convert shapefile to GeoJSON
federal_opportunity_zones_json = federal_opportunity_zones.to_json()

# Add GeoJSON layers for federal opportunity zones
folium.GeoJson(federal_opportunity_zones_json, name='Federal Opportunity Zones', style_function=lambda x: {'fillColor': 'darkblue', 'color': 'darkblue', 'weight': 1, 'fillOpacity': 0.5}).add_to(m2)

# Add legend for soil contamination spectrum
legend_html = """
<div style="position: fixed; bottom: 50px; left: 50px; z-index:1000; font-size: 14px; background-color: white; padding: 10px; border: 1px solid grey; border-radius: 5px;">
    <p><strong>Soil Contamination Spectrum</strong></p>
    <p><span style="color: blue;">0-50 ppm</span></p>
    <p><span style="color: lime;">50-200 ppm</span></p>
    <p><span style="color: orange;">200-400 ppm</span></p>
    <p><span style="color: red;">400+ ppm</span></p>
    <p><span style="color: darkblue;">Federal Opportunity Zone</span></p>
</div>
"""
m2.get_root().html.add_child(folium.Element(legend_html))

Out[22]:

<branca.element.Element at 0x14290f890>

V. Results

The results of the analysis are presented through interactive heatmaps. While the beginning static maps provided initial insights into soil contamination patterns across Atlanta, it became evident that static visualization was insufficient to capture the complexity and granularity of the data. Therefore, the focus shifted to developing interactive heatmaps based on latitude and longitude coordinates, which offer a more comprehensive understanding of soil contamination distribution.

The final interactive lat/long heatmap without overlays reveals the spatial distribution of lead contamination across Atlanta. This heatmap allows users to explore the concentration of lead levels at specific locations, providing valuable insights into the extent of soil contamination. By visualizing the data in this interactive format, users can identify hotspots of contamination and assess the potential risks associated with different areas.

In [23]:

Out[23]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Next, overlays of federal opportunity zones (FOZ) were incorporated onto the interactive lat/long heatmap to examine the overlaps between different incentive zones and lead contamination. The FOZ overlay allows analysis of whether areas designated as economically distressed communities coincide with high levels of lead contamination in the soil. This analysis provides valuable insights into the socioeconomic factors influencing soil contamination patterns and highlights areas where remediation efforts may be prioritized to address both environmental and economic concerns. Analyzing the soil contamination points across Atlanta in relation to federal opportunity zones reveals interesting patterns. While some areas within FOZ may exhibit elevated levels of lead contamination, there is no significant overlap between FOZ and the darkest "hotspots" of contamination. This suggests that economic incentives and policy levers targeting FOZ may not directly address soil contamination issues in the most heavily affected areas. However, further analysis is needed to understand the underlying factors contributing to soil contamination disparities across different zones and to identify targeted interventions for remediation efforts.

In [24]:

m2

Out[24]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Overall, the combination of interactive heatmaps and overlays provides a powerful tool for visualizing and analyzing soil contamination data in relation to economic variables such as federal opportunity zones. These insights can inform decision-making processes and guide policy interventions aimed at addressing lead in soil and promoting environmental justice in urban areas.

VI. Discussion

Interpreting the findings on the first map, it is evident that soil lead contamination affects not only the Westside Atlanta Superfund Site established in 2018 but also other areas across Atlanta. The heatmaps reveal substantial lead contamination particularly in Southeast Atlanta around the Peachtree Creek area and in suburban areas like Decatur. This broader distribution underscores the pervasive nature of soil contamination in urban environments and the need for both greater community awareness and comprehensive remediation efforts. Regarding the implications of soil contamination in the context of urban environmental economics, specifically regarding incentive zones for remediation efforts, the analysis suggests limited overlap between Federal Opportunity Zones (FOZ) and measured soil samples. This lack of alignment raises questions about the effectiveness of current economic incentives for remediation efforts, as the absence of FOZ establishment in significant contamination hotspots suggests that existing policy levers may not adequately incentivize remediation efforts. Additionally, lack of sufficient data on lead contamination within FOZ could be a reason for the misalignment and could be due to a lack of awareness among FOZ communities about resources like soilSHOP’s free soil testing. To address this gap in particular, it is crucial for the Saikawa Lab to continue its efforts to recruit diverse participants and raise awareness about soil contamination and testing initiatives. Overall, this spatial distribution of soil contamination provides valuable insights relevant for all community members. By identifying contamination hotspots across Atlanta and assessing their relationship with economic incentive zones, light is shed on the need for targeted remediation efforts and policy interventions to address soil contamination effectively. Moving forward, it is essential to prioritize community engagement and awareness-building efforts to ensure equitable access to remediation resources and protect public health and the environment.

VII. Conclusion

In summary, this study fuels a greater understanding of the spatial distribution of lead contamination across Atlanta using soilSHOP data. By visualizing contamination hotspots and their relationship with economic incentive zones, the groundwork is laid for a comprehensive understanding of soil contamination in urban environments. The maps generated serve as a tangible visualization of the extensive work conducted by Dr. Saikawa and the lab in collecting, testing, and recording heavy metal pollutants in soil. However, it is essential to acknowledge the limitations of the analysis. Findings are based solely on soilSHOP data, and future research should incorporate additional datasets and methodologies to provide a more holistic understanding of soil contamination in Atlanta. Moreover, as more data is collected and a better understanding of the economics of soil contamination is gained, it's crucial to consider the implications for policy and remediation efforts. Moving forward, continued data collection and analysis are recommended to refine understanding of soil contamination patterns and their socioeconomic impacts. Additionally, efforts should focus on raising awareness about soil contamination issues and promoting community engagement to ensure equitable access to remediation resources. By addressing these challenges and leveraging interdisciplinary approaches, progress can be made in mitigating soil contamination and safeguarding public health and the environment in urban areas.

VIII. Project Proposal for Subsequent Semester

Upon returning in Spring 2025 from my fall semester abroad, there are a few key areas I am interested in pursuing for a project with Dr. Saikawa. The focus will still be on soilSHOP results and building out the soilSHOP data to continuously update and grow the heatmap. However, in terms of the project’s greater impact, it will be focused on either correlating the soilSHOP data with socioeconomic aspects of the people submitting their soil (such as income ranges, education, and other socioeconomic factors), expanding the analysis of urban economic factors (such as looking into further different incentive zones and/or market data on property values), or taking on a more coding-intensive project to figure out a way to embed a real-time heatmap of soil contamination onto the Saikawa Lab website to be constantly updating as the Lab collects and measures more soil and perfecting the user interface where community members can go onto the website and immediately zoom in to see lead soil contamination severity levels across atlanta.

Correlating with socioeconomic factors and/or incentive data would aim to investigate how soil contamination disproportionately affects disadvantaged communities with less valuable land and how these findings can inform future policies. Meanwhile, focusing on the website and user-friendliness would explore the potential incorporation of soilSHOP results into wider Atlanta-area reports to enhance understanding and address soil contamination issues effectively. In terms of narrowing down this project scope, communication with Dr. Saikawa through my time abroad will be consistent to narrow down the project and finalize the details of the scope, especially now that I’ve gained my bearings in the lab this past semester.

IX. Annotated Bibliography

Hussain, A., Muslim, M., & Bangroo, S. (2023). “Assessment of Soil Contamination Using Remote Sensing and Spatial Techniques.” In Soil Contamination and Remediation Approaches (pp. 249-266). Springer. This chapter explores the challenges of soil contamination and the role of remote sensing and spatial techniques in assessing and managing it effectively. It discusses various methods such as hyperspectral, thermal, and radar remote sensing for mapping contamination over large areas. The chapter emphasizes the importance of these techniques in identifying contamination patterns and supporting decision-making processes. Despite advancements, challenges like spectral unmixing and data integration persist. Overall, it highlights the significance of remote sensing and spatial techniques in improving soil contamination assessment.

da Silva, F.B.V., et al. (2017). “Soil contamination by metals with high ecological risk in urban and rural areas.” International Journal of Environmental Science and Technology, 14(3), 553–562. This study examines the levels of metal contamination in soil samples from urban and rural areas, focusing on Cd, Cr, Cu, Pb, Ni, and Zn. Their findings highlight widespread contamination, with human-generated and natural sources both identified. The study emphasizes the ecological risks posed by contamination and underscores the urgent need for mitigation measures.

Balotin, L., Distler, S., Williams, A., Peters, S. J. W., Hunter, C. M., Theal, C., ... Saikawa, E. (2020). “Atlanta Residents' Knowledge Regarding Heavy Metal Exposures and Remediation in Urban Agriculture.” International Journal of Environmental Research and Public Health, 17(6), 2069. This research investigates Atlanta residents' awareness of heavy metal exposure risks in urban agriculture settings. Through surveys and interviews, the study explores knowledge gaps and attitudes towards contamination and remediation options. The findings underscore the importance of public education and engagement in addressing soil contamination issues, particularly in the Atlanta area.

Bonnieux, F., et al. (1998). “Reducing soil contamination: economic incentives and potential benefits.” Agriculture, Ecosystems & Environment, 67(2–3), 275-288. This article discusses the economic aspects of soil contamination reduction, focusing on the European Union context. The authors examine the microeconomics of pollution control and the challenges associated with estimating the costs and benefits of restoration efforts. Through the example of pesticide use in agriculture, they highlight the complexities of assessing the economic viability of soil restoration and emphasize the role of effective policy frameworks in addressing contamination.

Wu, Q. (2021). “Leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment.” Journal of Open Source Software, 6(64), 3414. This article provides a practical guide to utilizing Python and geopandas for spatial analysis, which will be helpful for understanding the methodology employed in this research project. Leafmap, a Python package designed for interactive mapping and geospatial analysis in Jupyter environments, serves as a free and open-source solution for loading and analyzing vector and raster data and designing interactive graphical user interfaces (GUI), enhancing accessibility to geospatial data analysis without requiring extensive coding. This article highlights the potential of Leafmap in democratizing geospatial analysis and fostering collaboration within the geospatial community.

Other References¶

Environmental Protection Agency. (n.d.). Site Information for Westside Atlanta Lead Superfund Site. Retrieved from https://cumulis.epa.gov/supercpad/CurSites/csitinfo.cfm?id=0407160
ArcGIS. (n.d.). Atlanta Planning and Geographic Information System Open Data. Retrieved from https://dpcd-coaplangis.opendata.arcgis.com/search

Exploring Soil Contamination and Urban Environmental Economics in Atlanta: A Spatial Analysis

Claudia Smith

Dr. Eri Saikawa - ENVS 399R - Spring 2024

Abstract:¶

Other References¶